When Camille Roux sets out to make a work of art, he often turns to the internet for advice. "What's your favorite?" he recently tweeted, sharing four computer-generated variations on an abstract visual theme, along with a poll allowing people to register their preferences. The denizens of Twitter began to weigh in. Some cast a vote without comment, while others offered Roux a rationale. One user said they preferred a particular image over another "because the red makes it look more lush."

Semiconductor Engineering sat down to discuss the issues and challenges with machine learning in semiconductor manufacturing with Kurt Ronse, director of the advanced lithography program at Imec; Yudong Hao, senior director of marketing at Onto Innovation; Romain Roux, data scientist at Mycronic; and Aki Fujimura, chief executive of D2S. What follows are excerpts of that conversation. SE: Machine learning is a hot topic. This technology uses a neural network to crunch data and identify patterns, then matches certain patterns and learns which of those attributes are important. We also have more advanced forms called deep learning.

Picture a pilot navigating the crowded skies over a major European city with a dark thundercloud looming ahead. Avoiding the storm is a matter of urgency, but how? Fortunately, the decision to change trajectory has just become easier, thanks to Thales's new-generation Flight Management System (FMS) for civil and military aircraft, PureFlyt, which provides pilots with more detailed weather information as the flight progresses. It does this with agile functions including flight planning and trajectory computation, fuel management, horizontal and vertical guidance, datalink connections with on-ground counterparts, and location capabilities. A user-friendly "What You See Is What You Fly" display shows the pilot precisely how the aircraft is forecast to behave throughout the duration of the flight up until wheels touch ground.

To answer the question, "Where's Waldo?" readers need to look for a number of distinguishing features. Several characters may be spotted with a striped scarf, striped hat, round-rimmed glasses, or a cane, but only Waldo will have all of these features. As described July 22, 2019, in Nature Microbiology, a team led by scientists at the U.S. Department of Energy (DOE) Joint Genome Institute (JGI), a DOE Office of Science User Facility, developed an algorithm that a computer could use to conduct a similar type of search in microbial and metagenomic databases. In this case, the machine "learned" to identify a certain type of bacterial viruses or phages called inoviruses, which are filamentous viruses with small, single-stranded DNA genomes and a unique chronic infection cycle. "We're not sure why we systematically manage to miss them; maybe it's due to the way we currently isolate and extract viruses," said the study's lead author Simon Roux, a JGI research scientist in the Environmental Genomics group.

PARIS - Brain implants, longer lives, genetically modified humans: For the prophets of "transhumanism" -- the scientifically assisted evolution of humans beyond our current limitations -- it is just a matter of time. But many scientists insist that some problems are not so easily solved. Sooner or later, they argue, the movement that crystallized in the can-do culture of 1980s California will hit the brick wall of the scientifically impossible. The most recent controversy was in November, when Chinese scientist He Jiankui claimed to have created the world's first genetically edited babies, who he said were HIV-resistant. The backlash from the scientific community led to his work being suspended, as questions were raised not just about the quality of the science, but the ethics of the research.

How do you look for a needle in a haystack, when you are not sure what the needle looks like? This is the problem that faces scientists as they try to deal with increasingly complex datasets. One answer is to turn machine learning loose on the enormous volumes of data they have captured. The problem of finding relevant data in genetic databases is one that Simon Roux, a researcher working at the U.S. Department of Energy's Joint Genome Institute, faced when investigating the role that an obscure and little-understood family of viruses plays in the environment. There are many types of virus, called bacteriophages, that infect bacteria.

Many viruses are difficult to study because they cannot be grown in the lab.Credit: Sebastian Kaulitzki/SPL/Getty Researchers have used artificial intelligence (AI) to discover nearly 6,000 previously unknown species of virus. The work, presented on 15 March at a meeting organized by the US Department of Energy (DOE), illustrates an emerging tool for exploring the enormous, largely unknown diversity of viruses on Earth. Although viruses influence everything from human health to the degradation of trash, they are hard to study. Scientists cannot grow most viruses in the lab, and attempts to identify their genetic sequences are often thwarted because their genomes are tiny and evolve fast. In recent years, researchers have hunted for unknown viruses by sequencing DNA in samples taken from various environments.